Transcriptional trajectories of human kidney injury progression
Pietro E. Cippà, … , Maarten Naesens, Andrew P. McMahon
Pietro E. Cippà, … , Maarten Naesens, Andrew P. McMahon
Published November 15, 2018
Citation Information: JCI Insight. 2018;3(22):e123151. https://doi.org/10.1172/jci.insight.123151.
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Clinical Research and Public Health Nephrology Transplantation

Transcriptional trajectories of human kidney injury progression

Abstract

BACKGROUND. The molecular understanding of the progression from acute to chronic organ injury is limited. Ischemia/reperfusion injury (IRI) triggered during kidney transplantation can contribute to progressive allograft dysfunction. METHODS. Protocol biopsies (n = 163) were obtained from 42 kidney allografts at 4 time points after transplantation. RNA sequencing–mediated (RNA-seq–mediated) transcriptional profiling and machine learning computational approaches were employed to analyze the molecular responses to IRI and to identify shared and divergent transcriptional trajectories associated with distinct clinical outcomes. The data were compared with the response to IRI in a mouse model of the acute to chronic kidney injury transition. RESULTS. In the first hours after reperfusion, all patients exhibited a similar transcriptional program under the control of immediate-early response genes. In the following months, we identified 2 main transcriptional trajectories leading to kidney recovery or to sustained injury with associated fibrosis and renal dysfunction. The molecular map generated by this computational approach highlighted early markers of kidney disease progression and delineated transcriptional programs associated with the transition to chronic injury. The characterization of a similar process in a mouse IRI model extended the relevance of our findings beyond transplantation. CONCLUSIONS. The integration of multiple transcriptomes from serial biopsies with advanced computational algorithms overcame the analytical hurdles related to variability between individuals and identified shared transcriptional elements of kidney disease progression in humans, which may prove as useful predictors of disease progression following kidney transplantation and kidney injury. This generally applicable approach opens the way for an unbiased analysis of human disease progression. FUNDING. The study was supported by the California Institute for Regenerative Medicine and by the Swiss National Science Foundation.

Authors

Pietro E. Cippà, Bo Sun, Jing Liu, Liang Chen, Maarten Naesens, Andrew P. McMahon

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Figure 3

Transcriptional trajectory of transition from acute to chronic kidney injury.

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Transcriptional trajectory of transition from acute to chronic kidney in...
(A) Minimum spanning tree of kidney biopsy transcriptomes at 3 (3M, dark blue, n = 38) and 12 (12M, light blue, n = 39) months after transplantation, and 10 samples collected after implantation (POST, green). Community A (marked in blue) separated from the rest of the study population (community B, in red). (B) Violin plots showing mRNA per sample (RPS) values of representative genes selected as markers for kidney injury (HAVCR1, VCAN), fibrosis (COL1A1), and chronic inflammation (CCL19). Adjusted P values are reported (Benjamini-Hochberg). (C and D) Pseudotime analysis including samples collected after implantation (POST) and 3 and 12 months after transplantation. Sample state ordering in the reduced dimensional space is shown. The colors indicate the time point of biopsy collection in C and the classification in communities A or B in D. (E) Similar analysis including only 3- and 12-month samples based on genes differentially expressed in communities A and B. The color of the dots indicate the progression along the pseudotime, as indicated. (F) Cluster analysis of the top 2,000 genes differentially expressed along the pseudotime shown in E. Genes are vertically aligned and classified in 4 clusters (the complete list of genes is reported in the Supplemental Table 8). (G) Representative example of 1 gene expressed early (HAVCR1) and late (MMP2) in the transition to chronic injury. The colors indicate the pseudotime, as indicated. The numbers indicate the cluster, according to F. (H) Glomerular filtration rate at 12 months, estimated by CKD-EPI equation (eGFR) in communities A and B. P value was calculated by Mann-Whitney U test. (I) Histogram of the degree of fibrosis 12 months after transplantation, quantified by ci-score on conventional histology. The percentage of patients in each fibrosis category in communities A and B is reported. The groups were compared by χ2 test. (J) Venn diagram including genes differentially expressed in community A compared with community B (human) and homologous mouse genes differentially expressed 12 months after IRI compared with control. Significance of enrichment was determined by hypergeometric test. (K and L) RPKM values along the late time-course analysis after IRI in mice (n = 3 for each time point).